Dawei Yin

Recommendations with Negative Feedback via Pairwise Deep Reinforcement Learning

2018

Recommender systems play a crucial role in mitigating the problem of information overload by suggesting users' personalized items or services. The vast majority of traditional recommender systems consider the recommendation procedure as a static process …

Dynamic Graph Neural Networks

2018 · arXiv (Cornell University)

Graphs, which describe pairwise relations between objects, are essential representations of many real-world data such as social networks. In recent years, graph neural networks, which extend the neural network models to graph data, have attracted …

Reinforcement Learning to Optimize Long-term User Engagement in Recommender Systems

2019 · arXiv (Cornell University)

Recommender systems play a crucial role in our daily lives. Feed streaming mechanism has been widely used in the recommender system, especially on the mobile Apps. The feed streaming setting provides users the interactive manner …

Pre-trained Language Model for Web-scale Retrieval in Baidu Search

2021

Retrieval is a crucial stage in web search that identifies a small set of query-relevant candidates from a billion-scale corpus. Discovering more semantically-related candidates in the retrieval stage is very promising to expose more high-quality …

AdaDiff: Adaptive Gradient Descent with the Differential of Gradient

2021 · Journal of Physics Conference Series

Abstract Optimization methods are crucial to train deep neural networks. Adaptive optimization methods, especially Adam, are wildly used because they aren’t sensitive to the selection of learning rate and converge fast. Recent work point out …

Incorporating Explicit Knowledge in Pre-trained Language Models for Passage Re-ranking

2022 · arXiv (Cornell University)

Passage re-ranking is to obtain a permutation over the candidate passage set from retrieval stage. Re-rankers have been boomed by Pre-trained Language Models (PLMs) due to their overwhelming advantages in natural language understanding. However, existing …

DiQAD: A Benchmark Dataset for Open-domain Dialogue Quality Assessment

2023

Dialogue assessment plays a critical role in the development of open-domain dialogue systems. Existing work are uncapable of providing an end-to-end and human-epistemic assessment dataset, while they only provide sub-metrics like coherence or the dialogues …

Iterative Self-Incentivization Empowers Large Language Models as Agentic Searchers

2025

Large language models (LLMs) have been widely integrated into information retrieval to advance traditional techniques. However, effectively enabling LLMs to seek accurate knowledge in complex tasks remains a challenge due to the complexity of multi-hop …

Streaming Recommender Systems

2017

The increasing popularity of real-world recommender systems produces data continuously and rapidly, and it becomes more realistic to study recommender systems under streaming scenarios. Data streams present distinct properties such as temporally ordered, continuous and …

Ranking Relevance in Yahoo Search

2016

Search engines play a crucial role in our daily lives. Relevance is the core problem of a commercial search engine. It has attracted thousands of researchers from both academia and industry and has been studied …

Recommendation with Social Dimensions

2016 · Proceedings of the AAAI Conference on Artificial Intelligence

The pervasive presence of social media greatly enriches online users' social activities, resulting in abundant social relations. Social relations provide an independent source for recommendation, bringing about new opportunities for recommender systems. Exploiting social relations …

Deep Reinforcement Learning for List-wise Recommendations

2017 · arXiv (Cornell University)

Recommender systems play a crucial role in mitigating the problem of information overload by suggesting users' personalized items or services. The vast majority of traditional recommender systems consider the recommendation procedure as a static process …

Micro Behaviors

2018

The explosive popularity of e-commerce sites has reshaped users» shopping habits and an increasing number of users prefer to spend more time shopping online. This evolution allows e-commerce sites to observe rich data about users. …

Hierarchical Variational Memory Network for Dialogue Generation

2018

Dialogue systems help various real applications interact with humans in an intelligent natural way. In dialogue systems, the task of dialogue generation aims to generate utterances given previous utterances as contexts. Among various spectrums of …

Sequicity: Simplifying Task-oriented Dialogue Systems with Single Sequence-to-Sequence Architectures

2018

Existing solutions to task-oriented dialogue systems follow pipeline designs which introduce architectural complexity and fragility. We propose a novel, holistic, extendable framework based on a single sequence-to-sequence (seq2seq) model which can be optimized with supervised …

Knowledge Diffusion for Neural Dialogue Generation

2018

End-to-end neural dialogue generation has shown promising results recently, but it does not employ knowledge to guide the generation and hence tends to generate short, general, and meaningless responses. In this paper, we propose a …

Deep reinforcement learning for page-wise recommendations

2018

Recommender systems can mitigate the information overload problem by suggesting users' personalized items. In real-world recommendations such as e-commerce, a typical interaction between the system and its users is - users are recommended a page …

Graph Neural Networks for Social Recommendation

2019

In recent years, Graph Neural Networks (GNNs), which can naturally integrate node information and topological structure, have been demonstrated to be powerful in learning on graph data. These advantages of GNNs provide great potential to …

Online Purchase Prediction via Multi-Scale Modeling of Behavior Dynamics

2019

Online purchase forecasting is of great importance in e-commerce platforms, which is the basis of how to present personalized interesting product lists to individual customers. However, predicting online purchases is not trivial as it is …

"Deep reinforcement learning for search, recommendation, and online advertising: a survey" by Xiangyu Zhao, Long Xia, Jiliang Tang, and Dawei Yin with Martin Vesely as coordinator

2019 · ACM SIGWEB Newsletter

Search, recommendation, and online advertising are the three most important information-providing mechanisms on the web. These information seeking techniques, satisfying users' information needs by suggesting users personalized objects (information or services) at the appropriate time …

Deep social collaborative filtering

2019

Recommender systems are crucial to alleviate the information overload problem in online worlds. Most of the modern recommender systems capture users' preference towards items via their interactions based on collaborative filtering techniques. In addition to …

Hypergraph Contrastive Collaborative Filtering

2022 · Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

Collaborative Filtering (CF) has emerged as fundamental paradigms for parameterizing users and items into latent representation space, with their correlative patterns from interaction data. Among various CF techniques, the development of GNN-based recommender systems, e.g., …

A Survey on Dialogue Systems

2017 · ACM SIGKDD Explorations Newsletter

Dialogue systems have attracted more and more attention. Recent advances on dialogue systems are overwhelmingly contributed by deep learning techniques, which have been employed to enhance a wide range of big data applications such as …

LLMRec: Large Language Models with Graph Augmentation for Recommendation

2024

The problem of data sparsity has long been a challenge in recommendation systems, and previous studies have attempted to address this issue by incorporating side information. However, this approach often introduces side effects such as …

A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Models

2024

As one of the most advanced techniques in AI, Retrieval-Augmented Generation (RAG) can offer reliable and up-to-date external knowledge, providing huge convenience for numerous tasks. Particularly in the era of AI-Generated Content (AIGC), the powerful …